Model Drift

Drift can appear when user behavior, markets, products, fraud patterns, sensors, or other real-world conditions evolve after deployment. Teams monitor input distributions and business outcomes so they can detect degradation and retrain, recalibrate, or replace models when needed. Understanding the concept helps practitioners choose training methods, evaluate model quality, and reduce errors when predictions are used in production systems.